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Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data.
Swedish University of Agricultural Sciences, Uppsala.
Dalarna University, School of Information and Engineering, Statistics.ORCID iD: 0000-0002-3183-3756
School of Mathematics & Statistics, University of Sheffield, Sheffield, UK.
Swedish University of Agricultural Sciences, Uppsala.
2022 (English)In: Movement Ecology, E-ISSN 2051-3933, Vol. 10, no 1, article id 40Article in journal (Refereed) Published
Abstract [en]

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.

Place, publisher, year, edition, pages
2022. Vol. 10, no 1, article id 40
Keywords [en]
Activity recognition, Hidden Markov models, Random forests, Rangifer tarandus, Support vector machines, Tri-axial accelerometer
National Category
Ecology
Identifiers
URN: urn:nbn:se:du-42769DOI: 10.1186/s40462-022-00339-0ISI: 000855816400002PubMedID: 36127747Scopus ID: 2-s2.0-85138410632OAI: oai:DiVA.org:du-42769DiVA, id: diva2:1699163
Available from: 2022-09-27 Created: 2022-09-27 Last updated: 2023-03-17Bibliographically approved

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Alam, Moudud

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf